Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	35.778080
daal4py_KNeighborsClassifier	0.0	3.0	5.774728
KNeighborsClassifier_kd_tree	0.0	6.0	56.388870
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	46.176190
KMeans_tall	0.0	1.0	38.758315
daal4py_KMeans_tall	0.0	1.0	14.115780
KMeans_short	0.0	0.0	16.751806
daal4py_KMeans_short	0.0	0.0	7.513559
LogisticRegression	0.0	1.0	2.259433
daal4py_LogisticRegression	0.0	0.0	54.962842
Ridge	0.0	0.0	46.445934
daal4py_Ridge	0.0	0.0	14.037203
total	0.0	31.0	39.032567

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.136	0.001	1000000	1000000	100	brute	-1	1	NaN	NaN	0.466	0.002	0.292	0.003	See
1	KNeighborsClassifier	predict	0.164	0.012	1000000	1	100	brute	-1	1	1.0	0.0	0.089	0.001	1.846	0.141	See
2	KNeighborsClassifier	predict	28.998	0.687	1000000	1000	100	brute	-1	1	1.0	0.0	1.805	0.015	16.065	0.402	See
3	KNeighborsClassifier	fit	0.133	0.003	1000000	1000000	100	brute	-1	5	NaN	NaN	0.469	0.004	0.284	0.006	See
4	KNeighborsClassifier	predict	0.172	0.012	1000000	1	100	brute	-1	5	1.0	1.0	0.089	0.001	1.935	0.134	See
5	KNeighborsClassifier	predict	36.367	0.000	1000000	1000	100	brute	-1	5	1.0	1.0	1.809	0.012	20.107	0.138	See
6	KNeighborsClassifier	fit	0.123	0.001	1000000	1000000	100	brute	-1	100	NaN	NaN	0.468	0.002	0.263	0.003	See
7	KNeighborsClassifier	predict	0.169	0.010	1000000	1	100	brute	-1	100	1.0	1.0	0.090	0.001	1.881	0.117	See
8	KNeighborsClassifier	predict	36.398	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	1.851	0.009	19.668	0.097	See
9	KNeighborsClassifier	fit	0.123	0.000	1000000	1000000	100	brute	1	1	NaN	NaN	0.468	0.003	0.262	0.002	See
10	KNeighborsClassifier	predict	0.178	0.001	1000000	1	100	brute	1	1	1.0	0.0	0.089	0.001	2.010	0.031	See
11	KNeighborsClassifier	predict	13.116	0.026	1000000	1000	100	brute	1	1	1.0	0.0	1.807	0.013	7.260	0.055	See
12	KNeighborsClassifier	fit	0.123	0.001	1000000	1000000	100	brute	1	5	NaN	NaN	0.470	0.001	0.261	0.001	See
13	KNeighborsClassifier	predict	0.188	0.001	1000000	1	100	brute	1	5	1.0	1.0	0.091	0.001	2.066	0.023	See
14	KNeighborsClassifier	predict	22.592	0.014	1000000	1000	100	brute	1	5	1.0	1.0	1.810	0.007	12.483	0.049	See
15	KNeighborsClassifier	fit	0.122	0.001	1000000	1000000	100	brute	1	100	NaN	NaN	0.475	0.004	0.257	0.003	See
16	KNeighborsClassifier	predict	0.189	0.001	1000000	1	100	brute	1	100	1.0	1.0	0.089	0.000	2.130	0.012	See
17	KNeighborsClassifier	predict	22.648	0.035	1000000	1000	100	brute	1	100	1.0	1.0	1.872	0.020	12.096	0.131	See
18	KNeighborsClassifier	fit	0.059	0.001	1000000	1000000	2	brute	-1	1	NaN	NaN	0.100	0.002	0.592	0.012	See
19	KNeighborsClassifier	predict	0.020	0.002	1000000	1	2	brute	-1	1	1.0	1.0	0.005	0.000	3.744	0.480	See
20	KNeighborsClassifier	predict	24.746	0.186	1000000	1000	2	brute	-1	1	1.0	1.0	0.263	0.002	94.089	0.971	See
21	KNeighborsClassifier	fit	0.055	0.001	1000000	1000000	2	brute	-1	5	NaN	NaN	0.098	0.002	0.565	0.012	See
22	KNeighborsClassifier	predict	0.030	0.003	1000000	1	2	brute	-1	5	1.0	1.0	0.005	0.001	5.832	0.796	See
23	KNeighborsClassifier	predict	31.623	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	0.266	0.001	118.988	0.545	See
24	KNeighborsClassifier	fit	0.055	0.001	1000000	1000000	2	brute	-1	100	NaN	NaN	0.097	0.000	0.573	0.006	See
25	KNeighborsClassifier	predict	0.029	0.001	1000000	1	2	brute	-1	100	1.0	1.0	0.006	0.001	5.215	0.609	See
26	KNeighborsClassifier	predict	31.846	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	0.313	0.002	101.801	0.720	See
27	KNeighborsClassifier	fit	0.055	0.000	1000000	1000000	2	brute	1	1	NaN	NaN	0.098	0.002	0.567	0.013	See
28	KNeighborsClassifier	predict	0.015	0.001	1000000	1	2	brute	1	1	1.0	1.0	0.005	0.000	2.910	0.250	See
29	KNeighborsClassifier	predict	10.391	0.028	1000000	1000	2	brute	1	1	1.0	1.0	0.267	0.003	38.987	0.516	See
30	KNeighborsClassifier	fit	0.057	0.000	1000000	1000000	2	brute	1	5	NaN	NaN	0.099	0.001	0.582	0.007	See
31	KNeighborsClassifier	predict	0.025	0.001	1000000	1	2	brute	1	5	1.0	1.0	0.006	0.000	4.585	0.397	See
32	KNeighborsClassifier	predict	17.989	0.035	1000000	1000	2	brute	1	5	1.0	1.0	0.265	0.003	67.801	0.673	See
33	KNeighborsClassifier	fit	0.055	0.001	1000000	1000000	2	brute	1	100	NaN	NaN	0.098	0.003	0.565	0.016	See
34	KNeighborsClassifier	predict	0.026	0.001	1000000	1	2	brute	1	100	1.0	1.0	0.006	0.000	4.650	0.418	See
35	KNeighborsClassifier	predict	17.998	0.045	1000000	1000	2	brute	1	100	1.0	1.0	0.313	0.003	57.480	0.528	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.188	0.053	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.739	0.009	4.312	0.090	See
1	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	8.385	4.505	See
2	KNeighborsClassifier_kd_tree	predict	0.474	0.006	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.128	0.003	3.705	0.097	See
3	KNeighborsClassifier_kd_tree	fit	3.154	0.056	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.776	0.011	4.064	0.092	See
4	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.000	0.000	6.928	4.973	See
5	KNeighborsClassifier_kd_tree	predict	0.949	0.015	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.222	0.002	4.280	0.083	See
6	KNeighborsClassifier_kd_tree	fit	3.232	0.133	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.767	0.020	4.216	0.205	See
7	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.001	0.000	5.587	2.753	See
8	KNeighborsClassifier_kd_tree	predict	3.081	0.028	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.689	0.006	4.473	0.057	See
9	KNeighborsClassifier_kd_tree	fit	3.125	0.057	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.792	0.009	3.945	0.085	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	2.334	1.836	See
11	KNeighborsClassifier_kd_tree	predict	0.832	0.006	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.128	0.002	6.475	0.102	See
12	KNeighborsClassifier_kd_tree	fit	3.128	0.051	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.768	0.019	4.073	0.121	See
13	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	1.0	1.0	0.000	0.000	2.594	1.756	See
14	KNeighborsClassifier_kd_tree	predict	1.639	0.016	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.226	0.005	7.252	0.178	See
15	KNeighborsClassifier_kd_tree	fit	3.184	0.044	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.778	0.010	4.090	0.078	See
16	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.001	0.000	2.598	1.583	See
17	KNeighborsClassifier_kd_tree	predict	5.487	0.073	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.650	0.010	8.443	0.173	See
18	KNeighborsClassifier_kd_tree	fit	1.245	0.021	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.472	0.017	2.640	0.105	See
19	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	18.757	16.927	See
20	KNeighborsClassifier_kd_tree	predict	0.025	0.000	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	32.283	15.549	See
21	KNeighborsClassifier_kd_tree	fit	1.225	0.021	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.455	0.004	2.689	0.052	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	17.383	16.171	See
23	KNeighborsClassifier_kd_tree	predict	0.027	0.001	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.000	24.498	9.699	See
24	KNeighborsClassifier_kd_tree	fit	1.251	0.034	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.457	0.005	2.735	0.080	See
25	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	17.586	15.106	See
26	KNeighborsClassifier_kd_tree	predict	0.047	0.001	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.006	0.001	7.367	0.876	See
27	KNeighborsClassifier_kd_tree	fit	1.274	0.028	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.460	0.008	2.771	0.077	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	5.711	5.554	See
29	KNeighborsClassifier_kd_tree	predict	0.024	0.000	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	32.123	14.855	See
30	KNeighborsClassifier_kd_tree	fit	1.265	0.025	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.460	0.007	2.749	0.069	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	5.553	5.408	See
32	KNeighborsClassifier_kd_tree	predict	0.026	0.001	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.000	23.215	8.228	See
33	KNeighborsClassifier_kd_tree	fit	1.246	0.036	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.459	0.004	2.714	0.082	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	4.830	4.153	See
35	KNeighborsClassifier_kd_tree	predict	0.054	0.001	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.006	0.001	8.433	0.766	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.574	0.009	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.417	0.034	1.375	0.115	See
1	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.938	1.792	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.750	1.376	See
3	KMeans_tall	fit	0.496	0.004	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.376	0.028	1.318	0.100	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.845	1.644	See
5	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.667	1.347	See
6	KMeans_tall	fit	6.312	0.083	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	2.960	0.027	2.132	0.034	See
7	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.786	1.489	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.166	1.662	See
9	KMeans_tall	fit	5.728	0.018	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	2.820	0.031	2.031	0.023	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.809	1.561	See
11	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.003	1.269	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.277	0.014	10000	10000	2	full	k-means++	30	300	1	0.0	30.0	NaN	29.0	NaN	0.097	0.002	2.847	0.158	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.749	1.392	See
2	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.065	0.379	See
3	KMeans_short	fit	0.107	0.001	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.040	0.000	2.669	0.032	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.762	1.418	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.046	0.350	See
6	KMeans_short	fit	0.710	0.031	10000	10000	100	full	k-means++	30	300	1	0.0	17.0	NaN	21.0	NaN	0.333	0.010	2.131	0.113	See
7	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.699	1.346	See
8	KMeans_short	predict	0.006	0.002	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	4.977	2.400	See
9	KMeans_short	fit	0.230	0.035	10000	10000	100	full	random	30	300	1	0.0	19.0	NaN	28.0	NaN	0.151	0.016	1.525	0.285	See
10	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.718	1.241	See
11	KMeans_short	predict	0.004	0.001	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	3.934	1.618	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.145	0.084	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	11.381	0.037	0.979	0.008	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.367	0.444	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.851	0.512	See
3	LogisticRegression	fit	0.813	0.019	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	0.793	0.056	1.026	0.076	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.002	0.079	0.116	See
5	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.003	0.000	0.504	0.132	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	1.706	0.035	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.863	0.010	1.977	0.047	See
1	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.492	0.555	See
2	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.001	0.000	0.912	0.545	See
3	Ridge	fit	1.134	0.013	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.225	0.002	5.046	0.080	See
4	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.605	0.724	See
5	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.659	0.533	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}